CPF_comparingrad

Pulling in data

## pull in jw hourly met
jw_hr <- read.table(
  "C:/Users/sears/Documents/Research/CPF/Data_downloads/joewright_met_hr_20220227.dat",
  sep = ",", header=TRUE, skip="1") %>%
  slice(., -(1:2)) %>%
  mutate(TIMESTAMP = ymd_hms(TIMESTAMP)) %>%
  mutate_if(is.character,as.numeric) %>%
  mutate(site = 'jw')

## pull in tc hourly met
tc_hr <- read.table(
  "C:/Users/sears/Documents/Research/CPF/Data_downloads/tunnelcreek_met2_hr_20220227.dat",
  sep = ",", header=TRUE, skip="1")%>%
  slice(., -(1:2)) %>%
  mutate(TIMESTAMP = ymd_hms(TIMESTAMP)) %>%
  mutate_if(is.character,as.numeric) %>%
  mutate(site = 'tc')

## pull in mc hourly met
mc_hr <- read.csv("C:/Users/sears/Documents/Research/CPF/Data_downloads/mtncampus_20220223.csv") %>%
  mutate(TIMESTAMP = ymd_hm(TIMESTAMP)) %>%
  mutate(hour = hour(TIMESTAMP),
         yday = yday(TIMESTAMP),
         year = year(TIMESTAMP)) %>%
  group_by(year, yday, hour) %>%
  summarize_all(list(mean)) %>%
  mutate(TIMESTAMP = floor_date(TIMESTAMP, "hour")) %>%
  mutate(site = 'mc')

## pull in persistent burn site
bf_hr <- read.table(
  "C:/Users/sears/Documents/Research/CPF/Data_downloads/wyatt/CR1000XSeries - new_Hourly_BF_2021.dat",
  sep = ",", header=TRUE, skip="1") %>%
  slice(., -(1:2)) %>%
  mutate(TIMESTAMP = ymd_hms(TIMESTAMP)) %>%
  mutate_if(is.character,as.numeric) %>%
  mutate(site = 'bf')

ub1_hr <- read.table(
  "C:/Users/sears/Documents/Research/CPF/Data_downloads/wyatt/CR1000_7_One_Hour_UB_0110.dat",
  sep = ",", header=TRUE, skip="1") %>%
  slice(., -(1:2)) %>%
  mutate(TIMESTAMP = ymd_hms(TIMESTAMP)) %>%
  mutate_if(is.character,as.numeric) %>%
  mutate(site = 'ub')

ub2_hr <- read.table(
  "C:/Users/sears/Documents/Research/CPF/Data_downloads/wyatt/CR1000_7_One_Hour_UB_0221.dat",
  sep = ",", header=TRUE, skip="1") %>%
  slice(., -(1:2)) %>%
  mutate(TIMESTAMP = ymd_hms(TIMESTAMP)) %>%
  mutate_if(is.character,as.numeric) %>%
  mutate(site = 'ub')

ub_hr <- bind_rows(ub1_hr, ub2_hr)

Binding data together

## compare incoming SW between sites, put in 1 df -- HOURLY
all_rad <- bind_rows(mc_hr, tc_hr, ub_hr, bf_hr, jw_hr) %>%
    select(c(TIMESTAMP, SWin_Avg, SWout_Avg, 
           LWin_Avg, LWout_Avg,
           SWalbedo_Avg, site)) %>%
  filter(TIMESTAMP > ymd_hms("2021-11-01 00:00:00")) %>%
  mutate(yday = yday(TIMESTAMP),
         date = as_date(TIMESTAMP))

## Adding missing grouping variables: `year`, `yday`

## get into daily sums
all_rad_daily <- all_rad %>%
  select(-TIMESTAMP, SWalbedo_Avg) %>%
  group_by(date, site) %>%
  summarize_all(list(sum))

Incoming shorwave rad

## plot SW in 1 plot
swin <- ggplot(all_rad, aes(x = TIMESTAMP, y= SWin_Avg, color = site)) +
  geom_line() +
  ggtitle("SWin hourly")

ggplotly(swin)

swin_daily<- ggplot(all_rad_daily, aes(x = date, y= SWin_Avg, color = site)) +
  geom_line() +
  ggtitle("SWin daily sum")

ggplotly(swin_daily)

Outgoing shortwave rad

## SW out as one plot
swout <- ggplot(all_rad, aes(x = TIMESTAMP, y= SWout_Avg, color = site)) +
  geom_line() +
  ggtitle("SWout hourly")

ggplotly(swout)

swout_daily<- ggplot(all_rad_daily, aes(x = date, y= SWout_Avg, color = site)) +
  geom_line() +
  ggtitle("SWout daily sum")

ggplotly(swout_daily)

Albedo

## plot albebo in 1 plot but limit the y vals
al <- ggplot(all_rad, aes(x = TIMESTAMP, y= SWalbedo_Avg, color = site)) +
  geom_line() +
  ylim(0,1) +
  ggtitle("Albedo hourly")

ggplotly(al)

#get an albedo daily avg
albedo_dailyavg <- all_rad %>%
  select(date, site, SWalbedo_Avg) %>%
  group_by(date, site) %>%
  summarize(SWalbedo_daily = mean(SWalbedo_Avg))

## Adding missing grouping variables: `year`, `yday`
## `summarise()` has grouped output by 'date'. You can override using the
## `.groups` argument.

## daily albedo avg plot
al_daily <- ggplot(albedo_dailyavg, aes(x = date, y= SWalbedo_daily, color = site)) +
  geom_line() +
  ylim(0,1) +
  ggtitle("Albedo daily average")

ggplotly(al_daily)

Net shortwave (computing SWin - SWout)

swnet <- ggplot(all_rad, aes(x = TIMESTAMP, y= SWin_Avg-SWout_Avg, color=site)) +
  geom_line() +
  ggtitle("SWnet hourly")

ggplotly(swnet)

swnet_daily<- ggplot(all_rad_daily, aes(x = date, y= SWin_Avg-SWout_Avg, color = site)) +
  geom_line() +
  ggtitle("SWnet daily sum")

ggplotly(swnet_daily)